Estimation method of multi-path non-Gaussian noise channel based on empirical likelihood method

Abstract

The invention provides a channel estimation method of a multi-path non-Gaussian noise channel, and designs an empirical likelihood estimation method combining training sequence data and observation data. The method comprises the following steps of: firstly obtaining the observation data passing through the non-Gaussian noise channel from a signal receiving end, and generating an auxiliary variable in combination with the training sequence data; secondly, converting the estimation problem into a non-parametric problem according to the empirical likelihood theory proposed by Owen, namely that a non-parametric empirical likelihood variable under certain limiting conditions is generated by use of the auxiliary variable, and the likelihood variable is obtained by the Lagrangian method; and finally, obtaining empirical likelihood values corresponding to different channel estimation values by the Newton iteration method, and taking the channel estimation value corresponding to the maximum empirical likelihood value. Take the mixture of additive white Gaussian noise and pulse noise as an example, the MSE (mean square error) and BER (bit error rate) effects of multi-path channel estimation are very good.

Description

technical field [0001] Aiming at the channel estimation problem of non-Gaussian noise channel, the present invention proposes a channel estimation method based on empirical likelihood method. Traditional multipath channel estimation methods, such as the Linear Square (LS) method, can achieve channel estimation in the case of additive Gaussian white noise. However, non-Gaussian noise, such as impulse noise, often exists in real channels. Traditional estimation methods are particularly sensitive to these non-Gaussian noises, so the estimation performance will be extremely deteriorated. The estimation method based on empirical likelihood can overcome this problem well and improve the performance of channel estimation. belongs to the field of communication. Background technique [0002] In a mobile communication system, in order to better detect the transmitted signal, the receiving end usually adopts coherent detection. However, the realization of coherent detection needs t...

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Problems solved by technology

Traditional methods are particularly sensitive to non-Gaussian noise, resulting in a significant drop in estimation performance

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